Robust Deep Hawkes Process under Label Noise of Both Event and Occurrence

TL;DR

This paper introduces the Robust Deep Hawkes Process (RDHP), a novel model designed to maintain high predictive accuracy in noisy real-world data, especially in medical diagnosis scenarios involving event and time label noise.

Contribution

The paper presents the first method to address both event and occurrence time label noise in deep Hawkes process models, enhancing robustness in practical applications.

Findings

RDHP effectively handles label noise in synthetic benchmarks.

RDHP improves prediction accuracy in real-world sleep disorder diagnosis.

The approach demonstrates robustness against both event and time label noise.

Abstract

Integrating deep neural networks with the Hawkes process has significantly improved predictive capabilities in finance, health informatics, and information technology. Nevertheless, these models often face challenges in real-world settings, particularly due to substantial label noise. This issue is of significant concern in the medical field, where label noise can arise from delayed updates in electronic medical records or misdiagnoses, leading to increased prediction risks. Our research indicates that deep Hawkes process models exhibit reduced robustness when dealing with label noise, particularly when it affects both event types and timing. To address these challenges, we first investigate the influence of label noise in approximated intensity functions and present a novel framework, the Robust Deep Hawkes Process (RDHP), to overcome the impact of label noise on the intensity function of Hawkes models, considering both the events and their occurrences. We tested RDHP using multiple open-source benchmarks with synthetic noise and conducted a case study on obstructive sleep apnea-hypopnea syndrome (OSAHS) in a real-world setting with inherent label noise. The results demonstrate that RDHP can effectively perform classification and regression tasks, even in the presence of noise related to events and their timing. To the best of our knowledge, this is the first study to successfully address both event and time label noise in deep Hawkes process models, offering a promising solution for medical applications, specifically in diagnosing OSAHS.